基于主成分特征向量系数的交通标志识别方法研究  

Study on traffic sign recognition based on principal component eigenvector coefficient

作　　者：邹柏贤[1] 苗军[2] 孟斌[1] Zou Baixian;Miao Jun;Meng Bin(College of Applied Arts and Science, Beijing Union University, Beijing 100191, China;School of Computer Science, Beijing Information Science and Technology University, Beijing 100101, China)

机构地区：[1]北京联合大学应用文理学院,北京1000191 [2]北京信息科技大学计算机学院,北京100101

出　　处：《微型机与应用》2017年第24期47-50,共4页Microcomputer & Its Applications

基　　金：国家自然科学基金项目(61650201;41671165);北京市自然科学基金项目(4162058)

摘　　要：对交通标志的识别研究一直是模式识别领域的研究热点。提出一种利用主成分特征向量系数和最近邻分类识别交通标志的方法,经验证取得较好的识别效果;同时,还研究探讨了交通标志图像的分辨率大小、主成分特征个数对正确识别率的影响。该方法的特点是交通标志图像来自真实环境,减小了计算量。The study on traffic sign recognition is always a research hotspot in the field of pattern recognition. A new method to recognize traffic signs using principal component eigenvector coefficient and the nearest neighbor classification was proposed. It was proved that the method has better recognition effect. The proposed method was simple and proved to be effective with traffic sign image in the real environment. Through experiments, the relationship between the resolution and the recognition rate of traffic sign images was discussed, and the relationship between the number of the principal components and the recognition rate was analyzed too. Reached a conclusion, when the resolution of the traffic sign image was 64 × 64, the recognition rate reached the maximum using one principal component feature.

关 键 词：交通标志图像 主成分特征向量系数 正确识别率 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]